Some Experimental Issues in Financial Fraud Detection: An Investigation
This work addresses methodological issues in financial fraud detection for auditors and researchers, but it is incremental as it revisits and compares existing metrics and methods without introducing new paradigms.
The paper critiques and analyzes key performance metrics for financial fraud detection, particularly in credit card fraud, and evaluates the effectiveness of various computational intelligence techniques and binary classification methods.
Financial fraud detection is an important problem with a number of design aspects to consider. Issues such as algorithm selection and performance analysis will affect the perceived ability of proposed solutions, so for auditors and re-searchers to be able to sufficiently detect financial fraud it is necessary that these issues be thoroughly explored. In this paper we will revisit the key performance metrics used for financial fraud detection with a focus on credit card fraud, critiquing the prevailing ideas and offering our own understandings. There are many different performance metrics that have been employed in prior financial fraud detection research. We will analyse several of the popular metrics and compare their effectiveness at measuring the ability of detection mechanisms. We further investigated the performance of a range of computational intelligence techniques when applied to this problem domain, and explored the efficacy of several binary classification methods.